README.md

---
license: cc-by-nc-4.0
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-405B-Instruct
pipeline_tag: text-generation
---

# CoALM-405B: The Largest Open-Source Agentic LLM

[![Made with Oumi](https://badgen.net/badge/Made%20with/Oumi/%23085CFF?icon=https%3A%2F%2Foumi.ai%2Flogo_dark.svg)](https://github.com/oumi-ai/oumi)

## 🌟 Model Overview

**CoALM-405B** is the **largest fully open-source Conversational Agentic Language Model**. This model sets a new standard in **Conversational AI**, seamlessly integrating both **Task-Oriented Dialogue (TOD) capabilities** and **Language Agent (LA) functionalities**. 
It is designed to **push the boundaries** of open-source agentic LLMs, excelling at **multi-turn dialogue, tool usage, reasoning, and API execution**. It is the **best-performing fully open-source LLM** on the **Berkeley Function Calling Leaderboard V3 (BFCL V3)**, marking a leap in open-source AI research.

## Model Sources

<!-- Provide the basic links for the model. -->

- 📝 **Paper:** https://arxiv.org/abs/2502.08820
- 🌐 **Project Page:** https://emrecanacikgoz.github.io/CoALM/
- 💻 **Repository:** https://github.com/oumi-ai/oumi/tree/main/configs/projects/CALM
- 💎 **Dataset:** https://huggingface.co/datasets/uiuc-convai/CoALM-IT


---
## 🚀 Model Details

- **Model Name:** CoALM-405B  
- **Developed by:** Colloboration of UIUC Conversational AI LAB and Oumi 
- **License:** cc-by-nc-4.0 
- **Architecture:** Meta-Llama 3.1-405B Instruct
- **Training Data:** CoALM-IT
- **Fine-tuning Framework:** [Oumi](https://github.com/oumi-ai/oumi)  
- **Training Hardware:** 8 NVIDIA H100 GPUs  
- **Training Duration:** ~6.5 days  
- **Evaluation Benchmarks:** MultiWOZ 2.4, BFCL V3, API-Bank  
- **Release Date:** February 5, 2025  

---
## 🏆 Why CoALM-405B is a Game-Changer

- **🚨 Largest Open-Source Agentic LLM:** A **405B** parameter model that brings state-of-the-art agentic capabilities to the public domain.
- **🎯 Best Open-Source Performance on BFCL V3:** Outperforms leading proprietary models like **GPT-4o, Gemini, and Claude** in function-calling tasks.
- **🔍 True Zero-Shot Function Calling:** Generalizes to unseen API tasks with **unmatched accuracy**.
- **🤖 Multi-Turn Dialogue Mastery:** Excels at long conversations, **task tracking, and complex reasoning**.
- **🛠 API Tool Use and Reasoning:** Makes precise API calls, interprets responses, and synthesizes **coherent** multi-step solutions.
- **📜 Fully Open-Source & Reproducible:** Released under **cc-by-nc-4.0**, including model weights, training logs, and datasets.


## 💡 CoALM-IT Dataset
<img src="table.png" alt="CALM-IT Dataset Statistics" width="800"/>


---
## 📊 Benchmark Performance

<img src="results.png" alt="CALM-IT Dataset Statistics" width="1000"/>

---
## 🔧 Training Process
### Fine-tuning Stages
1. **TOD Fine-tuning:** Optimized for **dialogue state tracking** (e.g., augmented SNIPS in instruction-tuned format).
2. **Function Calling Fine-tuning:** Trained to generate **highly accurate API calls** from LA datasets.
3. **ReAct-based Fine-tuning:** Enhances multi-turn conversations with structured **thought-action-observation-response reasoning**.

### Training Hyperparameters
- **Base Model:** Meta-Llama 3.1-405B Instruct
- **LoRA Config:** Rank = 16, Scaling Factor = 32
- **Batch Size:** 2
- **Learning Rate:** 1e-4
- **Optimizer:** AdamW (betas = 0.9, 0.999, epsilon = 1e-8)
- **Precision:** q4
- **Warm-up Steps:** 500
- **Gradient Accumulation Steps:** 1

---

## ❗️ How to Use CoALM-405B
It requires 16xH100 NVIDIA GPUs for Inference.

### 🏗 How to Load the Model using HuggingFace
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CoALM-8B")
model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CoALM-8B")
```

### 🛠 Example Oumi Inference
Oumi multi-node inference support is under development. 
CoALM-405B likely requires multi-node inference as most single nodes support up to 640GB of GPU VRAM.
To run multi-node inference, we recommend [vLLM](https://docs.vllm.ai/en/latest/serving/distributed_serving.html).

### 🛠 Example Oumi Fine-Tuning
```bash
pip install oumi

# See oumi_train.yaml in this model's /oumi/ directory.
oumi train -c ./oumi_train.yaml
```

More fine-tuning and **community-driven** optimizations are planned to enhance real-world usability.

## Acknowledgements
We'd like to thank the [Oumi AI Team](https://github.com/oumi-ai/oumi) for collaborating on training the models using the Oumi platform on [Together AI's](https://www.together.ai/) cloud.

## License
This model is licensed under [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).

---
## 📖 Citation
If you use **CoALM-405B** in your research, please cite:
```
@misc{acikgoz2025singlemodelmastermultiturn,
      title={Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model}, 
      author={Emre Can Acikgoz and Jeremiah Greer and Akul Datta and Ze Yang and William Zeng and Oussama Elachqar and Emmanouil Koukoumidis and Dilek Hakkani-Tür and Gokhan Tur},
      year={2025},
      eprint={2502.08820},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2502.08820}, 
}
```

For more details, visit [Project Repository](https://github.com/oumi-ai/oumi/tree/main/configs/projects/CALM) or contact **acikgoz2@illinois.edu**.